This invention relates generally to the detection of vehicles on a highway and, more particularly, to a system and method for classifying detected vehicles using a single sensor.
As noted in U.S. Pat. No. 5,278,555 (Hoekman), vehicle detectors are commonly inductive sensors that detect the presence of conductive or ferromagnetic articles within a specified area. For example, vehicle detectors can be used in traffic control systems to provide input data to control signal lights. Vehicle detectors are connected to one or more inductive sensors and operate on the principle of an inductance change caused by the movement of a vehicle in the vicinity of the inductive sensor. The inductive sensor can take a number of different forms, but commonly is a wire loop which is buried in the roadway and which acts as an inductor.
The vehicle detector generally includes circuitry which operates in conjunction with the inductive sensor to measure changes in inductance and to provide output signals as a function of those inductance changes. The vehicle detector includes an oscillator circuit which produces an oscillator output signal having a frequency which is dependent on sensor inductance. The sensor inductance is in turn dependent on whether the inductive sensor is loaded by the presence of a vehicle. The sensor is driven as a part of a resonant circuit of the oscillator. The vehicle detector measures changes in inductance in the sensor by monitoring the frequency of the oscillator output signal.
A critical parameter in nearly all traffic control strategies is vehicle speed. In most circumstances, traffic control equipment must make assumptions about vehicle speed (e.g., that the vehicle is traveling at the speed limit) while making calculations. Systems to detect vehicles and measurement of velocity on a real-time basis continue to evolve. A single loop inductive sensor can be used for such a purpose if an assumption is made that all vehicles have the same length. The velocity of the vehicle may then be estimated based on the time the vehicle is over the loop. Using this method, the velocity estimate for any given vehicle will have an error directly related to the difference of the vehicle""s actual length from the estimated length.
To improve accuracy, two loops (sensors) and two detector systems have been used in cooperation. These two-loop systems calculate velocity based upon the time of detection at the first loop, the time of detection at the second loop, and the distance between loops.
As noted in U.S. Pat. No. 5,455,768 (Johnson et al.), there are several systems that attempt to obtain information about the speed of a vehicle from a single detector. Generally, these system analyze the waveform of the detected vehicle to predict the speed of a passing vehicle. These systems estimate velocity independent of assumptions made concerning the vehicle length.
As noted in U.S. Pat. No. 5,801,943 (Nasburg), other technologies have been developed to replace loops. These sensors include microwave sensors, radar and laser radar sensors, piezoelectric sensors, ultrasonic sensors, and video processor loop replacement (tripwire) sensors. All of these sensors typically detect vehicles in a small area of the roadway network.
Video processor loop replacement sensors, also known as tripwire sensors, simulate inductive loops. With a tripwire sensor, a traffic manager can designate specific small areas within a video camera""s field of view. In use, a traffic manager typically electronically places the image of a loop over the roadway video. A video processor determines how many vehicles pass through the designated area by detecting changes within a detection box (image of a loop) as a vehicle passes through it. Like inductive loops, multiple tripwire sensors can be placed in each lane, allowing these systems to determine both vehicle counts and speeds.
Inexpensive RF transponders have been developed for use in electronic toll collection systems. When interrogated by an RF reader at the side of a roadway, RF transponders supply a unique identification signal which is fed to a processing station. It is understood that this system detects and identifies a given vehicle as it enters a toll area. After a vehicle is identified, the vehicle owner is debited for the proper amount of toll automatically.
Another technology being proposed for automated toll collection is the use of image processors to perform automated license plate reading. As with the RF transponders, a specific vehicle is identified by the system at the entrance to a toll road or parking area. Both the RF transponders and image processors provide vehicle identification and vehicle location information for a very limited area and have generally only been used for automatic debiting.
The multi-loop and complex sensors described above have the potential to supply useful information in the detection of vehicles. However, these sensors are typically expensive and would require significant installation efforts. Alternately stated, these sensors are largely unsupportable with the existing highway information single-loop infrastructure.
It would be advantageous if additional vehicle information could be derived from the single-loop sensor systems already installed in thousands of highways.
It would be advantageous if information from a single-loop sensor could be used to differentiate detected vehicles into classes of vehicles, such as passenger vehicles, trucks, multi-axle trucks, busses, and motorcycles.
It would be advantageous if the above-mentioned vehicle classification information could be used to accurately calculate vehicle velocities.
Accordingly, a method is provided for classifying or identifying a vehicle. The method comprises: establishing a plurality of classification groups; using a single inductive loop to generate a field for electrically sensing vehicles; measuring changes in the field; generating electronic signatures in response to measured changes in the field received from the single loop; analyzing the signatures; and classifying vehicles into a classification group in response to the analysis of the signatures.
In some aspects of the invention, establishing a plurality of vehicle classification groups includes establishing vehicle classifications selected from the group including passenger vehicles, two-axle trucks, three-axle vehicles, four-axle vehicles, five or more axle vehicles, buses, and motorcycles. Alternately, the classification can be based upon criteria such as vehicle mass, vehicle length, which is related to the number of axles, and the proximity of the vehicle body to the ground (the loop), which is an indication of weight.
Specifically, the method uses a neural network, which is a digital signal processing technique that can be trained to classify events. Therefore, the method includes an additional process of learning to form boundaries between the plurality of vehicle classification groups. Then, the analysis of the signatures includes recalling the boundary formation process when a signature is to be classified. The learning and recall processes are typically a multilayer perceptron (MLP) neural networking process.
In addition, the method further comprises: analyzing signatures to determine vehicle transition times across the loop; determining vehicle lengths in response to vehicle classifications; and calculating vehicle velocities in response to the determined vehicle lengths and the determined vehicle transition times.
Additional details of the above-described method and a system for classifying vehicles are presented below.